{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# using pycwr for Radar io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## read Radar basedata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pycwr.io import read_auto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PRD = read_auto(r\"E:\\RadarBaseData\\WSR98D\\Z9898\\Z_RADR_I_Z9898_20190828181529_O_DOR_SAD_CAP_FMT.bin.bz2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (sweep: 9)\n",
       "Coordinates:\n",
       "  * sweep              (sweep) int32 0 1 2 3 4 5 6 7 8\n",
       "Data variables:\n",
       "    latitude           float64 20.0\n",
       "    longitude          float64 110.2\n",
       "    altitude           int32 118\n",
       "    scan_type          &lt;U3 &#x27;ppi&#x27;\n",
       "    frequency          float64 2.73\n",
       "    start_time         datetime64[ns] 2019-08-28T18:16:02.393134\n",
       "    end_time           datetime64[ns] 2019-08-28T18:21:01.712371\n",
       "    nyquist_velocity   (sweep) float32 27.83 27.83 ... 32.416397 32.416397\n",
       "    unambiguous_range  (sweep) int32 300000 300000 146000 ... 124000 124000\n",
       "    rays_per_sweep     (sweep) int64 361 361 363 362 363 361 364 364 364\n",
       "    fixed_angle        (sweep) float32 0.48339844 1.4941406 ... 19.511719\n",
       "    beam_width         (sweep) float64 0.9972 0.9972 0.9917 ... 0.989 0.989</pre>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (sweep: 9)\n",
       "Coordinates:\n",
       "  * sweep              (sweep) int32 0 1 2 3 4 5 6 7 8\n",
       "Data variables:\n",
       "    latitude           float64 20.0\n",
       "    longitude          float64 110.2\n",
       "    altitude           int32 118\n",
       "    scan_type          <U3 'ppi'\n",
       "    frequency          float64 2.73\n",
       "    start_time         datetime64[ns] 2019-08-28T18:16:02.393134\n",
       "    end_time           datetime64[ns] 2019-08-28T18:21:01.712371\n",
       "    nyquist_velocity   (sweep) float32 27.83 27.83 ... 32.416397 32.416397\n",
       "    unambiguous_range  (sweep) int32 300000 300000 146000 ... 124000 124000\n",
       "    rays_per_sweep     (sweep) int64 361 361 363 362 363 361 364 364 364\n",
       "    fixed_angle        (sweep) float32 0.48339844 1.4941406 ... 19.511719\n",
       "    beam_width         (sweep) float64 0.9972 0.9972 0.9917 ... 0.989 0.989"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PRD.scan_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (range: 1200, time: 361)\n",
       "Coordinates:\n",
       "    azimuth    (time) float64 285.3 286.2 287.2 288.2 ... 283.1 284.1 285.1\n",
       "    elevation  (time) float64 0.48 0.48 0.48 0.48 0.48 ... 0.52 0.52 0.52 0.52\n",
       "    x          (time, range) float64 -241.2 -482.3 ... -2.891e+05 -2.894e+05\n",
       "    y          (time, range) float64 65.84 131.7 197.5 ... 7.813e+04 7.819e+04\n",
       "    z          (time, range) float64 238.1 240.2 242.3 ... 8.241e+03 8.252e+03\n",
       "    lat        (time, range) float64 20.0 20.0 20.0 20.0 ... 20.68 20.68 20.68\n",
       "    lon        (time, range) float64 110.2 110.2 110.2 ... 107.5 107.5 107.5\n",
       "  * range      (range) float64 250.0 500.0 750.0 ... 2.995e+05 2.998e+05 3e+05\n",
       "  * time       (time) datetime64[ns] 2019-08-28T18:16:02.393134 ... 2019-08-28T18:16:25.134606\n",
       "Data variables:\n",
       "    V          (time, range) float64 nan nan nan nan -5.5 ... nan nan nan nan\n",
       "    W          (time, range) float64 nan nan nan nan 1.0 ... nan nan nan nan nan\n",
       "    dBT        (time, range) float64 nan nan nan nan 34.0 ... nan nan nan nan\n",
       "    dBZ        (time, range) float64 nan nan nan nan -7.5 ... nan nan nan nan\n",
       "    SQI        (time, range) float64 nan nan nan nan 0.93 ... nan nan nan nan\n",
       "    ZDR        (time, range) float64 nan nan nan nan 0.93 ... nan nan nan nan\n",
       "    CC         (time, range) float64 nan nan nan nan 0.88 ... nan nan nan nan\n",
       "    PhiDP      (time, range) float64 nan nan nan nan 32.98 ... nan nan nan nan\n",
       "    KDP        (time, range) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    SNRH       (time, range) float64 nan nan nan nan 34.0 ... nan -7.5 nan nan</pre>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (range: 1200, time: 361)\n",
       "Coordinates:\n",
       "    azimuth    (time) float64 285.3 286.2 287.2 288.2 ... 283.1 284.1 285.1\n",
       "    elevation  (time) float64 0.48 0.48 0.48 0.48 0.48 ... 0.52 0.52 0.52 0.52\n",
       "    x          (time, range) float64 -241.2 -482.3 ... -2.891e+05 -2.894e+05\n",
       "    y          (time, range) float64 65.84 131.7 197.5 ... 7.813e+04 7.819e+04\n",
       "    z          (time, range) float64 238.1 240.2 242.3 ... 8.241e+03 8.252e+03\n",
       "    lat        (time, range) float64 20.0 20.0 20.0 20.0 ... 20.68 20.68 20.68\n",
       "    lon        (time, range) float64 110.2 110.2 110.2 ... 107.5 107.5 107.5\n",
       "  * range      (range) float64 250.0 500.0 750.0 ... 2.995e+05 2.998e+05 3e+05\n",
       "  * time       (time) datetime64[ns] 2019-08-28T18:16:02.393134 ... 2019-08-28T18:16:25.134606\n",
       "Data variables:\n",
       "    V          (time, range) float64 nan nan nan nan -5.5 ... nan nan nan nan\n",
       "    W          (time, range) float64 nan nan nan nan 1.0 ... nan nan nan nan nan\n",
       "    dBT        (time, range) float64 nan nan nan nan 34.0 ... nan nan nan nan\n",
       "    dBZ        (time, range) float64 nan nan nan nan -7.5 ... nan nan nan nan\n",
       "    SQI        (time, range) float64 nan nan nan nan 0.93 ... nan nan nan nan\n",
       "    ZDR        (time, range) float64 nan nan nan nan 0.93 ... nan nan nan nan\n",
       "    CC         (time, range) float64 nan nan nan nan 0.88 ... nan nan nan nan\n",
       "    PhiDP      (time, range) float64 nan nan nan nan 32.98 ... nan nan nan nan\n",
       "    KDP        (time, range) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
       "    SNRH       (time, range) float64 nan nan nan nan 34.0 ... nan -7.5 nan nan"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PRD.fields[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## using pycwr with Py-ART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
      "## library for working with weather radar data. Py-ART is partly\n",
      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
      "## Science user facility.\n",
      "##\n",
      "## If you use this software to prepare a publication, please cite:\n",
      "##\n",
      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pyart\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyart_radar = PRD.ToPyartRadar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zy\\Anaconda3\\lib\\site-packages\\pyart\\map\\gates_to_grid.py:177: DeprecationWarning: Barnes weighting function is deprecated. Please use Barnes 2 to be consistent with Pauley and Wu 1990.\n",
      "  \" Pauley and Wu 1990.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyart_radar.fields['reflectivity']['data'][:, -10:] = np.ma.masked\n",
    "\n",
    "# exclude masked gates from the gridding\n",
    "gatefilter = pyart.filters.GateFilter(pyart_radar)\n",
    "gatefilter.exclude_transition()\n",
    "gatefilter.exclude_masked('reflectivity')\n",
    "\n",
    "# perform Cartesian mapping, limit to the reflectivity field.\n",
    "grid = pyart.map.grid_from_radars(\n",
    "    (pyart_radar,), gatefilters=(gatefilter, ),\n",
    "    grid_shape=(1, 241, 241),\n",
    "    grid_limits=((2000, 2000), (-123000.0, 123000.0), (-123000.0, 123000.0)),\n",
    "    fields=['reflectivity'])\n",
    "\n",
    "# create the plot\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.imshow(grid.fields['reflectivity']['data'][0], origin='lower')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## save as cfradial format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyart.io.write_cfradial(\"cfradial.nc\", pyart_radar)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
